A Machine Learning Approach for Cognitive Decline Detection Using Neuroimaging Data Developed a multi-model system to predict brain tumor, dementia, and schizophrenia diseases. Integrated these models into a Flask app for real-time predictions.

This project implements a deep learning pipeline to automate the segmentation of brain tumors from multi-modal MRI scans. Using the BraTS 2020 dataset, the model distinguishes between different tumor sub-regions, including the necrotic core, peritumoral edema, and enhancing tumor.

A Deep Learning Model using Res Net-50 with transfer learning used for detecting sub types of brain tumors.

Attention-based Deep Learning Approaches in Brain Tumor Image Analysis: A Mini Review

A Novel Optimized ResNet-SD Model for Brain Tumor Classification Using Stochastic Depth and Metaheuristic Optimization

This project was presented at the 2025 6th International Conference on Computers and Artificial Intelligence Technology (CAIT 2025). The work has been accepted for publication in the IEEE conference proceedings and will be updated with the official DOI and publication link once available.

A deep learning-based approach for automatic detection of brain tumors from magnetic resonance imaging (MRI) scans. Brain Tumor Detection using Deep Learning Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

A Python implementation of the YOLO (You Only Look Once) object detection algorithm, designed for real-time detection and localization of brain tumors in images.

This detector will find brain tumors in MRI scans of the head with 100% successful analysis rate in the test set. Testing the tumor-detecting AI made with deep learning software was conducted with scans of 59 patients. Training the AI (neural net) took 184 patients diagnosed with cancer who supplied a total of 178 scans. -Dan

An AI-powered brain MRI analysis system. Brain tumor segmentation is performed using the LGG MRI dataset with a U-Net architecture. The model identifies tumor regions in MRI images by generating masks, calculates the proportion of cancerous areas, and provides a risk assessment.

3 Deep Learning models implemented - RADNet, ViT (Vision Transformer), Hybrid (RADNet + ViT) to further develop two Deep Learning models function of classifying 4 types of brain tumors including only pictures of no brain tumor (no other information about patients provided, only pictures).

Brain tumor detection using CNN for multi-class classification.

NeuroDetect AI is a deep-learning based system that detects brain tumors and Alzheimer’s disease from MRI scans using optimized CNN and transfer-learning models. It provides fast and reliable predictions through a simple web interface with a Flask backend, offering an effective AI solution for early neurological diagnosis.

Segment brain tumors in MRI scans using a 2D U-Net on the BraTS 2020 dataset for precise multi-class tumor sub-region identification.

Explore Attention-based Deep Learning for brain tumor image analysis. Enhance diagnosis accuracy and efficiency. 🌟🖥️